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ISACA Implementing the NIST Cybersecurity Framework using COBIT 2019

  • Exam Number/Code : NIST-COBIT-2019
  • Exam Name : ISACA Implementing the NIST Cybersecurity Framework using COBIT 2019
  • Questions and Answers : 213 Q&As
  • Update Time: 2019-01-10
  • Price: $ 99.00 $ 39.00

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NEW QUESTION: 1
Wenn Sie eine brauchbare Fähigkeitsanalyse unter Verwendung von Attributdaten durchführen, müssen Sie einen ziemlich großen Stichprobensatz erhalten, um statistisch fundiert zu sein.
A. Richtig
B. Falsch
Answer: A

NEW QUESTION: 2
オンラインフォトアルバムアプリには、高品質のディスプレイで複数の画面(デスクトップ、携帯電話、タブレットなど)をサポートするための重要なデザイン機能があります。画像の複数のバージョンは、異なる解像度とレイアウトで保存する必要があります。
画像処理のJavaプログラムは、画像のサイズとフォーマットにもよりますが、1回のアップロードにつき平均5秒かかります。各画像アップロードは、次の画像メタデータをキャプチャします:ユーザー、アルバム、写真ラベル、アップロードタイムスタンプアプリは以下の要件をサポートするべきです:
- 毎秒何百ものユーザー画像のアップロード
- 最大画像メタデータサイズ10 MB
- 最大画像メタデータサイズ1 KB
- 画像アップロード後1分以内に、サポートされているすべての画面で最適化された解像度で画像が表示されるこれらの要件を満たすために使用する戦略はどれですか。
A. Amazonキネシスに画像とメタデータを書き込みます。 Spark StreamingでAmazon Elastic MapReduce(EMR)を使用して画像処理を実行し、画像出力をAmazonに保存する
B. メタデータ付きの画像をAmazon S3にアップロードするには、Lambda関数を使用して画像処理を実行し、Amazon S3への画像出力とアプリリポジトリDBへのメタデータを保存します
C. Amazon Kinesisに画像とメタデータを書き込みます。KinesisClient Library(KCL)アプリケーションを使用して画像処理を実行し、画像出力をAmazon S3に保存してメタデータをアプリリポジトリDBに保存します
D. BLOBデータ型でイメージとメタデータのRDSを書き込みます。 AWS Data Pipelineを使用して画像処理を実行し、画像出力をAmazon S3に保存し、メタデータをアプリリポジトリDBに保存する
Answer: B

NEW QUESTION: 3



A. Option D
B. Option B
C. Option A
D. Option C
Answer: D